Overview

Dataset statistics

Number of variables59
Number of observations14875
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory451.0 B

Variable types

Numeric13
DateTime3
Categorical40
Boolean3

Alerts

ACS is highly overall correlated with STEMIHigh correlation
AKI is highly overall correlated with CKD and 2 other fieldsHigh correlation
ANAEMIA is highly overall correlated with HBHigh correlation
BNP is highly overall correlated with HEART FAILUREHigh correlation
CARDIOGENIC SHOCK is highly overall correlated with SHOCKHigh correlation
CKD is highly overall correlated with AKI and 2 other fieldsHigh correlation
CREATININE is highly overall correlated with AKI and 2 other fieldsHigh correlation
DURATION OF STAY is highly overall correlated with duration of intensive unit stayHigh correlation
EF is highly overall correlated with PRIOR CMPHigh correlation
HB is highly overall correlated with ANAEMIA and 1 other fieldsHigh correlation
HEART FAILURE is highly overall correlated with BNP and 2 other fieldsHigh correlation
HFNEF is highly overall correlated with HEART FAILUREHigh correlation
HFREF is highly overall correlated with HEART FAILUREHigh correlation
OUTCOME_DAMA is highly overall correlated with OUTCOME_DISCHARGEHigh correlation
OUTCOME_DISCHARGE is highly overall correlated with OUTCOME_DAMA and 1 other fieldsHigh correlation
OUTCOME_EXPIRY is highly overall correlated with OUTCOME_DISCHARGE and 1 other fieldsHigh correlation
PRIOR CMP is highly overall correlated with EFHigh correlation
SEVERE ANAEMIA is highly overall correlated with HBHigh correlation
SHOCK is highly overall correlated with CARDIOGENIC SHOCK and 1 other fieldsHigh correlation
STEMI is highly overall correlated with ACSHigh correlation
UREA is highly overall correlated with AKI and 2 other fieldsHigh correlation
Year is highly overall correlated with monthHigh correlation
duration of intensive unit stay is highly overall correlated with DURATION OF STAYHigh correlation
month is highly overall correlated with YearHigh correlation
SMOKING is highly imbalanced (70.4%)Imbalance
ALCOHOL is highly imbalanced (64.5%)Imbalance
CKD is highly imbalanced (53.3%)Imbalance
SEVERE ANAEMIA is highly imbalanced (86.4%)Imbalance
STABLE ANGINA is highly imbalanced (59.2%)Imbalance
ATYPICAL CHEST PAIN is highly imbalanced (82.8%)Imbalance
VALVULAR is highly imbalanced (77.2%)Imbalance
CHB is highly imbalanced (82.8%)Imbalance
SSS is highly imbalanced (94.0%)Imbalance
CVA INFRACT is highly imbalanced (80.4%)Imbalance
CVA BLEED is highly imbalanced (95.9%)Imbalance
AF is highly imbalanced (70.5%)Imbalance
VT is highly imbalanced (78.9%)Imbalance
PSVT is highly imbalanced (93.7%)Imbalance
CONGENITAL is highly imbalanced (91.4%)Imbalance
UTI is highly imbalanced (66.0%)Imbalance
NEURO CARDIOGENIC SYNCOPE is highly imbalanced (92.8%)Imbalance
ORTHOSTATIC is highly imbalanced (93.0%)Imbalance
INFECTIVE ENDOCARDITIS is highly imbalanced (98.1%)Imbalance
DVT is highly imbalanced (89.8%)Imbalance
CARDIOGENIC SHOCK is highly imbalanced (66.3%)Imbalance
SHOCK is highly imbalanced (71.8%)Imbalance
PULMONARY EMBOLISM is highly imbalanced (88.5%)Imbalance
CHEST INFECTION is highly imbalanced (84.6%)Imbalance
OUTCOME_DAMA is highly imbalanced (68.4%)Imbalance
OUTCOME_EXPIRY is highly imbalanced (61.8%)Imbalance
duration of intensive unit stay has 2629 (17.7%) zerosZeros

Reproduction

Analysis started2025-11-25 19:40:27.856082
Analysis finished2025-11-25 19:40:57.272332
Duration29.42 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

MRD No.
Real number (ℝ)

Distinct12244
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407774.32
Minimum506
Maximum6408503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:40:57.458580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile126633.9
Q1276887.5
median400037.5
Q3552773
95-th percentile669517.5
Maximum6408503
Range6407997
Interquartile range (IQR)275885.5

Descriptive statistics

Standard deviation198982.09
Coefficient of variation (CV)0.48797111
Kurtosis152.46926
Mean407774.32
Median Absolute Deviation (MAD)135452.5
Skewness5.9291791
Sum6.065643 × 109
Variance3.9593871 × 1010
MonotonicityNot monotonic
2025-11-25T22:40:57.590708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25662815
 
0.1%
27301615
 
0.1%
32571511
 
0.1%
1779269
 
0.1%
2114189
 
0.1%
4158658
 
0.1%
427578
 
0.1%
1537168
 
0.1%
1560418
 
0.1%
1819738
 
0.1%
Other values (12234)14776
99.3%
ValueCountFrequency (%)
5061
 
< 0.1%
7983
< 0.1%
9892
< 0.1%
10061
 
< 0.1%
10601
 
< 0.1%
11961
 
< 0.1%
12611
 
< 0.1%
18431
 
< 0.1%
31801
 
< 0.1%
44991
 
< 0.1%
ValueCountFrequency (%)
64085031
< 0.1%
57115871
< 0.1%
48889261
< 0.1%
48882861
< 0.1%
48880781
< 0.1%
45620141
< 0.1%
9874561
< 0.1%
9576211
< 0.1%
8739671
< 0.1%
8286601
< 0.1%

D.O.A
Date

Distinct660
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
Minimum2017-01-04 00:00:00
Maximum2019-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T22:40:57.717622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:57.859192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

D.O.D
Date

Distinct668
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
Minimum2017-01-05 00:00:00
Maximum2019-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T22:40:57.989847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:58.121110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AGE
Real number (ℝ)

Distinct96
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.42084
Minimum4
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:40:58.245227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile38
Q154
median62
Q370
95-th percentile82
Maximum110
Range106
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.425534
Coefficient of variation (CV)0.21858271
Kurtosis0.62946925
Mean61.42084
Median Absolute Deviation (MAD)8
Skewness-0.50185831
Sum913635
Variance180.24496
MonotonicityNot monotonic
2025-11-25T22:40:58.404512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65864
 
5.8%
60823
 
5.5%
70735
 
4.9%
55603
 
4.1%
62564
 
3.8%
75516
 
3.5%
58445
 
3.0%
50421
 
2.8%
72406
 
2.7%
63399
 
2.7%
Other values (86)9099
61.2%
ValueCountFrequency (%)
47
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
74
< 0.1%
91
 
< 0.1%
104
< 0.1%
114
< 0.1%
123
< 0.1%
135
< 0.1%
142
 
< 0.1%
ValueCountFrequency (%)
1102
 
< 0.1%
994
 
< 0.1%
982
 
< 0.1%
974
 
< 0.1%
964
 
< 0.1%
9511
0.1%
949
0.1%
931
 
< 0.1%
9214
0.1%
917
< 0.1%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
9425 
1
5450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Length

2025-11-25T22:40:58.532197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:40:58.617612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Most occurring characters

ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09425
63.4%
15450
36.6%

Location
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
1
11348 
0
3527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%

Length

2025-11-25T22:40:58.705027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:40:58.780875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%

Most occurring characters

ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
111348
76.3%
03527
 
23.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
10326 
1
4549 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010326
69.4%
14549
30.6%

Length

2025-11-25T22:40:58.864809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:40:58.938329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010326
69.4%
14549
30.6%

Most occurring characters

ValueCountFrequency (%)
010326
69.4%
14549
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010326
69.4%
14549
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010326
69.4%
14549
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010326
69.4%
14549
30.6%
Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
Minimum2017-04-01 00:00:00
Maximum2019-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T22:40:59.008975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:59.121832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

DURATION OF STAY
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3815798
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:40:59.246472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile15
Maximum98
Range97
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.9968402
Coefficient of variation (CV)0.78300989
Kurtosis21.460539
Mean6.3815798
Median Absolute Deviation (MAD)2
Skewness3.1505589
Sum94926
Variance24.968412
MonotonicityNot monotonic
2025-11-25T22:40:59.413010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22010
13.5%
51812
12.2%
31787
12.0%
41736
11.7%
61531
10.3%
71212
8.1%
8996
6.7%
9684
 
4.6%
1557
 
3.7%
10544
 
3.7%
Other values (43)2006
13.5%
ValueCountFrequency (%)
1557
 
3.7%
22010
13.5%
31787
12.0%
41736
11.7%
51812
12.2%
61531
10.3%
71212
8.1%
8996
6.7%
9684
 
4.6%
10544
 
3.7%
ValueCountFrequency (%)
981
 
< 0.1%
671
 
< 0.1%
581
 
< 0.1%
531
 
< 0.1%
524
< 0.1%
502
< 0.1%
491
 
< 0.1%
483
< 0.1%
471
 
< 0.1%
461
 
< 0.1%

duration of intensive unit stay
Real number (ℝ)

High correlation  Zeros 

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7811092
Minimum0
Maximum58
Zeros2629
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:40:59.646845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile11
Maximum58
Range58
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9955711
Coefficient of variation (CV)1.0567193
Kurtosis15.779411
Mean3.7811092
Median Absolute Deviation (MAD)2
Skewness2.8615631
Sum56244
Variance15.964589
MonotonicityNot monotonic
2025-11-25T22:40:59.770933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
02629
17.7%
22463
16.6%
31828
12.3%
41655
11.1%
11630
11.0%
51438
9.7%
6862
 
5.8%
7597
 
4.0%
8447
 
3.0%
9325
 
2.2%
Other values (35)1001
 
6.7%
ValueCountFrequency (%)
02629
17.7%
11630
11.0%
22463
16.6%
31828
12.3%
41655
11.1%
51438
9.7%
6862
 
5.8%
7597
 
4.0%
8447
 
3.0%
9325
 
2.2%
ValueCountFrequency (%)
581
< 0.1%
482
< 0.1%
451
< 0.1%
421
< 0.1%
411
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
372
< 0.1%
362
< 0.1%

SMOKING
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14099 
1
 
776

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

Length

2025-11-25T22:40:59.894555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:40:59.964063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

Most occurring characters

ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014099
94.8%
1776
 
5.2%

ALCOHOL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
13878 
1
 
997

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

Length

2025-11-25T22:41:00.063205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:00.137281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

Most occurring characters

ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013878
93.3%
1997
 
6.7%

DM
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
9945 
1
4930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09945
66.9%
14930
33.1%

Length

2025-11-25T22:41:00.218946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:00.295062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09945
66.9%
14930
33.1%

Most occurring characters

ValueCountFrequency (%)
09945
66.9%
14930
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09945
66.9%
14930
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09945
66.9%
14930
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09945
66.9%
14930
33.1%

HTN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
7660 
1
7215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
07660
51.5%
17215
48.5%

Length

2025-11-25T22:41:00.385823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:00.454984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07660
51.5%
17215
48.5%

Most occurring characters

ValueCountFrequency (%)
07660
51.5%
17215
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07660
51.5%
17215
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07660
51.5%
17215
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07660
51.5%
17215
48.5%

CAD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
1
9919 
0
4956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
19919
66.7%
04956
33.3%

Length

2025-11-25T22:41:00.543208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:00.613347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
19919
66.7%
04956
33.3%

Most occurring characters

ValueCountFrequency (%)
19919
66.7%
04956
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19919
66.7%
04956
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19919
66.7%
04956
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19919
66.7%
04956
33.3%

PRIOR CMP
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
12557 
1
2318 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

Length

2025-11-25T22:41:00.701797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:00.773696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

Most occurring characters

ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012557
84.4%
12318
 
15.6%

CKD
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
13396 
1
1479 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

Length

2025-11-25T22:41:01.167453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:01.238084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

Most occurring characters

ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013396
90.1%
11479
 
9.9%

HB
Real number (ℝ)

High correlation 

Distinct181
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.234833
Minimum3
Maximum26.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:01.331879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8.3
Q110.7
median12.4
Q313.8
95-th percentile15.7
Maximum26.5
Range23.5
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.3072303
Coefficient of variation (CV)0.18857882
Kurtosis0.28973443
Mean12.234833
Median Absolute Deviation (MAD)1.5
Skewness-0.23577379
Sum181993.14
Variance5.3233117
MonotonicityNot monotonic
2025-11-25T22:41:01.459138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4531
 
3.6%
12.6286
 
1.9%
12.9274
 
1.8%
13.3271
 
1.8%
13.6270
 
1.8%
13.1270
 
1.8%
12.1269
 
1.8%
13.9264
 
1.8%
13263
 
1.8%
11.9255
 
1.7%
Other values (171)11922
80.1%
ValueCountFrequency (%)
31
 
< 0.1%
3.42
 
< 0.1%
3.61
 
< 0.1%
3.71
 
< 0.1%
3.81
 
< 0.1%
3.91
 
< 0.1%
41
 
< 0.1%
4.33
< 0.1%
4.45
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
26.51
 
< 0.1%
224
< 0.1%
21.21
 
< 0.1%
20.92
< 0.1%
20.71
 
< 0.1%
20.61
 
< 0.1%
20.51
 
< 0.1%
20.13
< 0.1%
201
 
< 0.1%
19.81
 
< 0.1%

TLC
Real number (ℝ)

Distinct451
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.543216
Minimum0.1
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:01.583120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.5
Q18
median10.1
Q313.4
95-th percentile21.5
Maximum314
Range313.9
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation7.5243394
Coefficient of variation (CV)0.65184082
Kurtosis352.15378
Mean11.543216
Median Absolute Deviation (MAD)2.5
Skewness12.621269
Sum171705.34
Variance56.615683
MonotonicityNot monotonic
2025-11-25T22:41:01.708174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1433
 
2.9%
8.4197
 
1.3%
8.9195
 
1.3%
7.9193
 
1.3%
8.6190
 
1.3%
9.4189
 
1.3%
8187
 
1.3%
7.8184
 
1.2%
9.1183
 
1.2%
8.1182
 
1.2%
Other values (441)12742
85.7%
ValueCountFrequency (%)
0.11
 
< 0.1%
0.31
 
< 0.1%
0.41
 
< 0.1%
0.61
 
< 0.1%
0.71
 
< 0.1%
0.92
< 0.1%
0.941
 
< 0.1%
13
< 0.1%
1.11
 
< 0.1%
1.21
 
< 0.1%
ValueCountFrequency (%)
3141
< 0.1%
2611
< 0.1%
2101
< 0.1%
2001
< 0.1%
1391
< 0.1%
1322
< 0.1%
1251
< 0.1%
1231
< 0.1%
1201
< 0.1%
1001
< 0.1%

PLATELETS
Real number (ℝ)

Distinct657
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.00915
Minimum0.58
Maximum1179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:01.837107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile100
Q1173
median225
Q3286
95-th percentile417
Maximum1179
Range1178.42
Interquartile range (IQR)113

Descriptive statistics

Standard deviation102.8861
Coefficient of variation (CV)0.43227792
Kurtosis6.3661071
Mean238.00915
Median Absolute Deviation (MAD)57
Skewness1.4891768
Sum3540386.1
Variance10585.55
MonotonicityNot monotonic
2025-11-25T22:41:01.967272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225366
 
2.5%
150200
 
1.3%
14099
 
0.7%
22690
 
0.6%
21089
 
0.6%
24286
 
0.6%
22084
 
0.6%
18984
 
0.6%
22982
 
0.6%
21581
 
0.5%
Other values (647)13614
91.5%
ValueCountFrequency (%)
0.581
< 0.1%
1.381
< 0.1%
1.71
< 0.1%
2.22
< 0.1%
3.61
< 0.1%
5.21
< 0.1%
6.51
< 0.1%
6.91
< 0.1%
71
< 0.1%
81
< 0.1%
ValueCountFrequency (%)
11792
< 0.1%
11111
< 0.1%
10942
< 0.1%
10861
< 0.1%
10521
< 0.1%
10161
< 0.1%
10062
< 0.1%
9991
< 0.1%
9861
< 0.1%
9721
< 0.1%

GLUCOSE
Real number (ℝ)

Distinct520
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.49272
Minimum1.2
Maximum888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:02.093117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile84
Q1108
median136
Q3190
95-th percentile325
Maximum888
Range886.8
Interquartile range (IQR)82

Descriptive statistics

Standard deviation82.008821
Coefficient of variation (CV)0.50781745
Kurtosis6.5276275
Mean161.49272
Median Absolute Deviation (MAD)35
Skewness2.0771472
Sum2402204.2
Variance6725.4467
MonotonicityNot monotonic
2025-11-25T22:41:02.219752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361013
 
6.8%
110257
 
1.7%
98214
 
1.4%
96189
 
1.3%
100182
 
1.2%
102178
 
1.2%
108178
 
1.2%
104174
 
1.2%
94161
 
1.1%
120157
 
1.1%
Other values (510)12172
81.8%
ValueCountFrequency (%)
1.21
 
< 0.1%
171
 
< 0.1%
221
 
< 0.1%
254
< 0.1%
25.11
 
< 0.1%
265
< 0.1%
274
< 0.1%
283
< 0.1%
292
 
< 0.1%
302
 
< 0.1%
ValueCountFrequency (%)
8881
< 0.1%
8091
< 0.1%
8081
< 0.1%
8071
< 0.1%
7511
< 0.1%
7261
< 0.1%
7161
< 0.1%
7151
< 0.1%
7121
< 0.1%
7091
< 0.1%

UREA
Real number (ℝ)

High correlation 

Distinct328
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.034925
Minimum0.1
Maximum495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:02.351588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile17
Q126
median35
Q357
95-th percentile137
Maximum495
Range494.9
Interquartile range (IQR)31

Descriptive statistics

Standard deviation42.400903
Coefficient of variation (CV)0.84742613
Kurtosis13.026889
Mean50.034925
Median Absolute Deviation (MAD)12
Skewness2.9490679
Sum744269.51
Variance1797.8366
MonotonicityNot monotonic
2025-11-25T22:41:02.481266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35578
 
3.9%
27425
 
2.9%
26425
 
2.9%
25415
 
2.8%
22410
 
2.8%
24407
 
2.7%
28399
 
2.7%
30398
 
2.7%
29391
 
2.6%
23386
 
2.6%
Other values (318)10641
71.5%
ValueCountFrequency (%)
0.11
< 0.1%
0.681
< 0.1%
31
< 0.1%
3.11
< 0.1%
3.61
< 0.1%
4.81
< 0.1%
51
< 0.1%
5.81
< 0.1%
61
< 0.1%
71
< 0.1%
ValueCountFrequency (%)
4951
 
< 0.1%
4792
< 0.1%
4502
< 0.1%
4311
 
< 0.1%
4301
 
< 0.1%
4161
 
< 0.1%
4031
 
< 0.1%
3751
 
< 0.1%
3643
< 0.1%
3551
 
< 0.1%

CREATININE
Real number (ℝ)

High correlation 

Distinct470
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3449377
Minimum0.065
Maximum15.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:02.604037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.065
5-th percentile0.567
Q10.78
median1
Q31.4
95-th percentile3.5
Maximum15.63
Range15.565
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation1.1986236
Coefficient of variation (CV)0.89121124
Kurtosis22.245828
Mean1.3449377
Median Absolute Deviation (MAD)0.3
Skewness3.9678736
Sum20005.949
Variance1.4366986
MonotonicityNot monotonic
2025-11-25T22:41:02.732259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81447
 
9.7%
0.71324
 
8.9%
0.91202
 
8.1%
11115
 
7.5%
0.6922
 
6.2%
1.1718
 
4.8%
1.2541
 
3.6%
0.5439
 
3.0%
1.3414
 
2.8%
1.4339
 
2.3%
Other values (460)6414
43.1%
ValueCountFrequency (%)
0.0651
 
< 0.1%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.12
 
< 0.1%
0.141
 
< 0.1%
0.162
 
< 0.1%
0.181
 
< 0.1%
0.29
0.1%
0.241
 
< 0.1%
0.263
 
< 0.1%
ValueCountFrequency (%)
15.631
< 0.1%
15.51
< 0.1%
14.11
< 0.1%
13.91
< 0.1%
13.31
< 0.1%
13.291
< 0.1%
13.21
< 0.1%
12.51
< 0.1%
12.481
< 0.1%
12.11
< 0.1%

BNP
Real number (ℝ)

High correlation 

Distinct1270
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean620.27374
Minimum4
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:02.866322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile48
Q1475
median475
Q3475
95-th percentile1883
Maximum5000
Range4996
Interquartile range (IQR)0

Descriptive statistics

Standard deviation667.56511
Coefficient of variation (CV)1.0762427
Kurtosis17.365816
Mean620.27374
Median Absolute Deviation (MAD)0
Skewness3.7831156
Sum9226571.9
Variance445643.18
MonotonicityNot monotonic
2025-11-25T22:41:02.996839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4758715
58.6%
5112
 
0.8%
500089
 
0.6%
7869
 
0.5%
7030
 
0.2%
79027
 
0.2%
78025
 
0.2%
625
 
0.2%
3024
 
0.2%
10624
 
0.2%
Other values (1260)5735
38.6%
ValueCountFrequency (%)
41
 
< 0.1%
5112
0.8%
625
 
0.2%
721
 
0.1%
814
 
0.1%
918
 
0.1%
1020
 
0.1%
1114
 
0.1%
1223
 
0.2%
1318
 
0.1%
ValueCountFrequency (%)
500089
0.6%
49602
 
< 0.1%
49001
 
< 0.1%
48901
 
< 0.1%
48201
 
< 0.1%
47901
 
< 0.1%
47801
 
< 0.1%
47502
 
< 0.1%
47301
 
< 0.1%
46601
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
11879 
1
2996 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

Length

2025-11-25T22:41:03.120435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:03.191049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

Most occurring characters

ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011879
79.9%
12996
 
20.1%

EF
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.237808
Minimum14
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.4 KiB
2025-11-25T22:41:03.276723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22
Q134
median42
Q360
95-th percentile60
Maximum60
Range46
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.76685
Coefficient of variation (CV)0.29527052
Kurtosis-1.1612823
Mean43.237808
Median Absolute Deviation (MAD)10
Skewness-0.014064291
Sum643162.4
Variance162.99247
MonotonicityNot monotonic
2025-11-25T22:41:03.394288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
604109
27.6%
421994
13.4%
30866
 
5.8%
35866
 
5.8%
45745
 
5.0%
25601
 
4.0%
40600
 
4.0%
32597
 
4.0%
28546
 
3.7%
38458
 
3.1%
Other values (36)3493
23.5%
ValueCountFrequency (%)
143
 
< 0.1%
1512
 
0.1%
1633
 
0.2%
18118
 
0.8%
192
 
< 0.1%
20323
2.2%
213
 
< 0.1%
22279
1.9%
234
 
< 0.1%
2484
 
0.6%
ValueCountFrequency (%)
604109
27.6%
592
 
< 0.1%
5833
 
0.2%
571
 
< 0.1%
5616
 
0.1%
55172
 
1.2%
5452
 
0.3%
535
 
< 0.1%
52153
 
1.0%
50418
 
2.8%

SEVERE ANAEMIA
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14593 
1
 
282

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

Length

2025-11-25T22:41:03.516760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:03.586919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

Most occurring characters

ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014593
98.1%
1282
 
1.9%

ANAEMIA
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
12263 
1
2612 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

Length

2025-11-25T22:41:03.676713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:03.747546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

Most occurring characters

ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012263
82.4%
12612
 
17.6%

STABLE ANGINA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
13662 
1
 
1213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

Length

2025-11-25T22:41:03.840485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:03.912550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

Most occurring characters

ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013662
91.8%
11213
 
8.2%

ACS
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
9443 
1
5432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09443
63.5%
15432
36.5%

Length

2025-11-25T22:41:04.000130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.073608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09443
63.5%
15432
36.5%

Most occurring characters

ValueCountFrequency (%)
09443
63.5%
15432
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09443
63.5%
15432
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09443
63.5%
15432
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09443
63.5%
15432
36.5%

STEMI
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
12779 
1
2096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

Length

2025-11-25T22:41:04.165780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.239125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

Most occurring characters

ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012779
85.9%
12096
 
14.1%

ATYPICAL CHEST PAIN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14495 
1
 
380

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

Length

2025-11-25T22:41:04.328926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.399597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

Most occurring characters

ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014495
97.4%
1380
 
2.6%

HEART FAILURE
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
10591 
1
4284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010591
71.2%
14284
28.8%

Length

2025-11-25T22:41:04.488192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.559383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010591
71.2%
14284
28.8%

Most occurring characters

ValueCountFrequency (%)
010591
71.2%
14284
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010591
71.2%
14284
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010591
71.2%
14284
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010591
71.2%
14284
28.8%

HFREF
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
12578 
1
2297 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

Length

2025-11-25T22:41:04.654903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.723574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

Most occurring characters

ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012578
84.6%
12297
 
15.4%

HFNEF
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
12876 
1
1999 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

Length

2025-11-25T22:41:04.811695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:04.882484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

Most occurring characters

ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012876
86.6%
11999
 
13.4%

VALVULAR
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14326 
1
 
549

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

Length

2025-11-25T22:41:04.971987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.040915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

Most occurring characters

ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014326
96.3%
1549
 
3.7%

CHB
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14493 
1
 
382

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

Length

2025-11-25T22:41:05.123798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.195374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

Most occurring characters

ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014493
97.4%
1382
 
2.6%

SSS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14772 
1
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

Length

2025-11-25T22:41:05.279625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.350729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

Most occurring characters

ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014772
99.3%
1103
 
0.7%

AKI
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
11525 
1
3350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

Length

2025-11-25T22:41:05.433749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.502904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

Most occurring characters

ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011525
77.5%
13350
 
22.5%

CVA INFRACT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14423 
1
 
452

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

Length

2025-11-25T22:41:05.590044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.661430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

Most occurring characters

ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014423
97.0%
1452
 
3.0%

CVA BLEED
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14810 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

Length

2025-11-25T22:41:05.742965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.814586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

Most occurring characters

ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014810
99.6%
165
 
0.4%

AF
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14100 
1
 
775

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

Length

2025-11-25T22:41:05.902728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:05.969498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

Most occurring characters

ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014100
94.8%
1775
 
5.2%

VT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14379 
1
 
496

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

Length

2025-11-25T22:41:06.056783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.126210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

Most occurring characters

ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014379
96.7%
1496
 
3.3%

PSVT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14765 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

Length

2025-11-25T22:41:06.217027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.287536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

Most occurring characters

ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014765
99.3%
1110
 
0.7%

CONGENITAL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14714 
1
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

Length

2025-11-25T22:41:06.374276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.444662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

Most occurring characters

ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014714
98.9%
1161
 
1.1%

UTI
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
13936 
1
 
939

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

Length

2025-11-25T22:41:06.530318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.598929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

Most occurring characters

ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013936
93.7%
1939
 
6.3%

NEURO CARDIOGENIC SYNCOPE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14746 
1
 
129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

Length

2025-11-25T22:41:06.684788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.756159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

Most occurring characters

ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014746
99.1%
1129
 
0.9%

ORTHOSTATIC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14751 
1
 
124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

Length

2025-11-25T22:41:06.841621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:06.913541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

Most occurring characters

ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014751
99.2%
1124
 
0.8%

INFECTIVE ENDOCARDITIS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14848 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

Length

2025-11-25T22:41:07.003736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:07.074853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

Most occurring characters

ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014848
99.8%
127
 
0.2%

DVT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14677 
1
 
198

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

Length

2025-11-25T22:41:07.544551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:07.614124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

Most occurring characters

ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014677
98.7%
1198
 
1.3%

CARDIOGENIC SHOCK
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
13948 
1
 
927

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

Length

2025-11-25T22:41:07.700191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:07.768724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

Most occurring characters

ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013948
93.8%
1927
 
6.2%

SHOCK
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14146 
1
 
729

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

Length

2025-11-25T22:41:07.854026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:07.925954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

Most occurring characters

ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014146
95.1%
1729
 
4.9%

PULMONARY EMBOLISM
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0
14646 
1
 
229

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14875
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

Length

2025-11-25T22:41:08.014059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:08.083672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

Most occurring characters

ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014646
98.5%
1229
 
1.5%

CHEST INFECTION
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
0.0
14544 
1.0
 
331

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters44625
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014544
97.8%
1.0331
 
2.2%

Length

2025-11-25T22:41:08.162212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:08.228791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.014544
97.8%
1.0331
 
2.2%

Most occurring characters

ValueCountFrequency (%)
029419
65.9%
.14875
33.3%
1331
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)44625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
029419
65.9%
.14875
33.3%
1331
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
029419
65.9%
.14875
33.3%
1331
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
029419
65.9%
.14875
33.3%
1331
 
0.7%

OUTCOME_DAMA
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.7 KiB
False
14024 
True
 
851
ValueCountFrequency (%)
False14024
94.3%
True851
 
5.7%
2025-11-25T22:41:08.268708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

OUTCOME_DISCHARGE
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.7 KiB
True
12919 
False
1956 
ValueCountFrequency (%)
True12919
86.9%
False1956
 
13.1%
2025-11-25T22:41:08.313167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

OUTCOME_EXPIRY
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.7 KiB
False
13770 
True
 
1105
ValueCountFrequency (%)
False13770
92.6%
True1105
 
7.4%
2025-11-25T22:41:08.356907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2212437
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.3 KiB
2025-11-25T22:41:08.421724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.276313
Coefficient of variation (CV)0.52663312
Kurtosis-0.93509127
Mean6.2212437
Median Absolute Deviation (MAD)3
Skewness-0.022396448
Sum92541
Variance10.734227
MonotonicityNot monotonic
2025-11-25T22:41:08.503779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
74431
29.8%
11543
 
10.4%
21367
 
9.2%
31186
 
8.0%
121038
 
7.0%
10875
 
5.9%
11809
 
5.4%
9769
 
5.2%
8762
 
5.1%
5731
 
4.9%
Other values (2)1364
 
9.2%
ValueCountFrequency (%)
11543
 
10.4%
21367
 
9.2%
31186
 
8.0%
4636
 
4.3%
5731
 
4.9%
6728
 
4.9%
74431
29.8%
8762
 
5.1%
9769
 
5.2%
10875
 
5.9%
ValueCountFrequency (%)
121038
 
7.0%
11809
 
5.4%
10875
 
5.9%
9769
 
5.2%
8762
 
5.1%
74431
29.8%
6728
 
4.9%
5731
 
4.9%
4636
 
4.3%
31186
 
8.0%

Year
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.4 KiB
2018
7177 
2017
5559 
2019
2139 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters59500
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
20187177
48.2%
20175559
37.4%
20192139
 
14.4%

Length

2025-11-25T22:41:08.613048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T22:41:08.686370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20187177
48.2%
20175559
37.4%
20192139
 
14.4%

Most occurring characters

ValueCountFrequency (%)
214875
25.0%
014875
25.0%
114875
25.0%
87177
12.1%
75559
 
9.3%
92139
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)59500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
214875
25.0%
014875
25.0%
114875
25.0%
87177
12.1%
75559
 
9.3%
92139
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)59500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
214875
25.0%
014875
25.0%
114875
25.0%
87177
12.1%
75559
 
9.3%
92139
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)59500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
214875
25.0%
014875
25.0%
114875
25.0%
87177
12.1%
75559
 
9.3%
92139
 
3.6%

Interactions

2025-11-25T22:40:54.857692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:38.356335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-25T22:40:41.204230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:42.445073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:43.913784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:45.149156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:46.683569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:48.107680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-25T22:40:45.445925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-25T22:40:52.622218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:53.898282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-25T22:40:55.590150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-25T22:40:52.029978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:53.334717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:54.539276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:56.163581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:39.663100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:41.024004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:42.264241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:43.725707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:44.972976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:46.469105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:47.914912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:49.211603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:50.783107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:52.135377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:53.434371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:54.668934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:56.252769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:39.752195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:41.114919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:42.355796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:43.817752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:45.061427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:46.558211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:48.010416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:49.315002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:50.875977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:52.236980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:53.530312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T22:40:54.768023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T22:41:08.852536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACSAFAGEAKIALCOHOLANAEMIAATYPICAL CHEST PAINBNPCADCARDIOGENIC SHOCKCHBCHEST INFECTIONCKDCONGENITALCREATININECVA BLEEDCVA INFRACTDMDURATION OF STAYDVTEFGENDERGLUCOSEHBHEART FAILUREHFNEFHFREFHTNINFECTIVE ENDOCARDITISLocationMRD No.NEURO CARDIOGENIC SYNCOPEORTHOSTATICOUTCOME_DAMAOUTCOME_DISCHARGEOUTCOME_EXPIRYPLATELETSPRIOR CMPPSVTPULMONARY EMBOLISMRAISED CARDIAC ENZYMESSEVERE ANAEMIASHOCKSMOKINGSSSSTABLE ANGINASTEMITLCTYPE OF ADMISSION-EMERGENCY/OPDUREAUTIVALVULARVTYearduration of intensive unit staymonth
ACS1.0000.0470.1020.0220.0190.0350.1180.0230.1920.0630.0030.0400.0600.0510.0510.0270.0560.0040.0120.0690.2710.0800.0930.0590.0370.0210.0260.0070.0300.0180.1100.0470.0310.0410.0790.0640.0490.0360.0460.0830.4050.0150.0270.0280.0290.2010.5340.0200.1850.0550.0470.0420.0520.0370.0680.051
AF0.0471.0000.0990.0670.0000.0000.0300.0290.0840.0100.0240.0000.0220.0000.0620.0000.0610.0000.0300.0100.0480.0160.0190.0230.0750.0380.0560.0000.0000.0300.0110.0000.0000.0000.0430.0630.0000.0370.0120.0190.0000.0000.0260.0190.0080.0590.0240.0000.0800.0570.0240.0890.0000.0920.0690.071
AGE0.1020.0991.0000.1800.1000.0820.0870.0780.2440.0370.0570.0080.0970.2940.2870.0120.0370.1620.1310.090-0.0800.0270.107-0.2200.1460.0810.1110.2170.1150.049-0.0680.0000.0000.0220.0800.080-0.0410.0420.0250.0370.0740.0440.0460.1010.0190.0930.0860.0320.1070.3120.0730.0260.0260.0230.142-0.019
AKI0.0220.0670.1801.0000.0160.2690.0740.2810.0170.1330.0280.0030.5800.0200.8850.0090.0340.1270.1580.0300.2650.0460.1010.3070.2050.0700.1910.0730.0040.0000.0380.0100.0000.0700.2000.1950.1160.2050.0080.0000.0800.0820.1490.0410.0000.1120.0410.0680.1410.7000.0920.0130.0610.0150.1800.055
ALCOHOL0.0190.0000.1000.0161.0000.0440.0000.0480.0140.0140.0190.0000.0190.0000.0000.0000.0000.0260.0170.0230.0350.1930.0150.1270.0450.0280.0290.0260.0000.0400.0330.0000.0000.0170.0340.0620.0340.0000.0090.0000.0000.0070.0170.3250.0000.0100.0390.0310.0210.0430.0170.0000.0080.0820.0120.042
ANAEMIA0.0350.0000.0820.2690.0441.0000.0410.1720.0160.0570.0000.0160.2760.0150.3060.0000.0060.0970.1570.0140.0540.1300.0550.9980.1200.0460.1070.0520.0270.0270.0110.0110.0130.0330.0850.0790.1570.0350.0000.0000.0440.3000.0550.0410.0000.0880.0600.0400.0810.3230.0700.0020.0120.0430.1570.023
ATYPICAL CHEST PAIN0.1180.0300.0870.0740.0000.0411.0000.0710.0910.0290.0240.0150.0470.0120.0620.0000.0260.0470.0490.0150.1280.0320.0480.0460.0880.0510.0610.0000.0000.0000.0000.0010.0090.0340.0590.0440.0280.0600.0080.0170.0650.0160.0250.0000.0070.0470.0640.0040.0810.0820.0080.0150.0180.0000.0660.018
BNP0.0230.0290.0780.2810.0480.1720.0711.0000.0230.0890.0210.0230.2220.0000.2340.0230.0080.0510.1220.013-0.2830.0360.063-0.1420.5340.3840.3100.0200.0100.015-0.0920.0280.0280.0690.1670.161-0.0210.2920.0130.0150.1370.0290.1070.0320.0000.1340.0000.0620.1520.2460.0260.0110.0560.1150.1400.015
CAD0.1920.0840.2440.0170.0140.0160.0910.0231.0000.0770.0140.0000.0000.0460.0000.0170.0530.1060.0260.0950.2250.1090.1030.0450.0150.0130.0000.3230.0510.0470.0270.0170.0000.0580.1470.1380.0850.0810.0510.1010.0690.0130.0930.0220.0000.1360.1670.0290.0100.0280.0000.0790.0050.1200.0000.139
CARDIOGENIC SHOCK0.0630.0100.0370.1330.0140.0570.0290.0890.0771.0000.0230.0330.0790.0090.1330.0000.0000.0000.0650.0140.1410.0060.0270.0590.0910.0000.1210.0240.0000.0000.0090.0070.0000.0000.3140.4030.0790.0990.0120.0000.0420.0120.6080.0190.0000.0530.0780.0450.0770.1420.0110.0130.0970.0810.0630.149
CHB0.0030.0240.0570.0280.0190.0000.0240.0210.0140.0231.0000.0120.0000.0070.0240.0000.0160.0070.0950.0000.0390.0110.0400.0260.0000.0000.0000.0100.0000.0090.0480.0000.0000.0260.0470.0370.0270.0190.0080.0130.0000.0120.0150.0200.0000.0450.0590.0000.0630.0530.0000.0060.0220.0190.0980.021
CHEST INFECTION0.0400.0000.0080.0030.0000.0160.0150.0230.0000.0330.0121.0000.0120.0040.0080.0000.0000.0000.0620.0000.0230.0000.0190.0300.0330.0280.0110.0070.0000.0000.0120.0080.0080.0000.0050.0140.0240.0030.0000.0000.0150.0000.0000.0000.0000.0230.0050.0510.0300.0330.0000.0000.0000.0300.0330.032
CKD0.0600.0220.0970.5800.0190.2760.0470.2220.0000.0790.0000.0121.0000.0210.8210.0000.0030.1120.1260.0270.1720.0300.0580.3030.1210.0510.1020.0810.0000.0000.0150.0130.0000.0650.1200.0960.0710.1120.0080.0130.0350.0890.0890.0310.0000.0720.0590.0500.0840.6260.0760.0000.0240.0220.1310.029
CONGENITAL0.0510.0000.2940.0200.0000.0150.0120.0000.0460.0090.0070.0040.0211.0000.0160.0000.0000.0140.0000.0000.0210.0150.0430.0590.0360.0220.0190.0380.0000.0220.0210.0200.0130.0000.0230.0220.0200.0130.0000.0000.0280.0000.0070.0020.0000.0210.0080.0000.0170.0220.0130.0250.0000.0190.0000.031
CREATININE0.0510.0620.2870.8850.0000.3060.0620.2340.0000.1330.0240.0080.8210.0161.0000.0000.0350.1240.1830.023-0.2640.0400.155-0.2480.1860.0690.1690.0760.0000.012-0.0940.0160.0000.0840.1970.189-0.1210.1990.0000.0000.0710.1000.1480.0330.0000.1070.0480.1800.1390.7410.0910.0180.0610.0150.2130.014
CVA BLEED0.0270.0000.0120.0090.0000.0000.0000.0230.0170.0000.0000.0000.0000.0000.0001.0000.0920.0000.0410.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0200.0070.0000.0260.0250.0000.0000.0000.0000.0000.0000.0000.0100.0000.0160.0110.0130.0000.0120.0000.0000.0000.0290.0120.000
CVA INFRACT0.0560.0610.0370.0340.0000.0060.0260.0080.0530.0000.0160.0000.0030.0000.0350.0921.0000.0220.0560.0090.0180.0000.0300.0000.0000.0000.0120.0300.0130.0050.0350.0070.0000.0200.0350.0250.0300.0070.0000.0080.0000.0080.0000.0000.0000.0400.0290.0000.0180.0280.0260.0160.0060.0720.0610.067
DM0.0040.0000.1620.1270.0260.0970.0470.0510.1060.0000.0070.0000.1120.0140.1240.0000.0221.0000.0600.0320.1140.0090.3930.1390.0390.0070.0620.1540.0200.0290.1060.0000.0100.0130.0180.0390.0180.0540.0080.0100.0280.0000.0060.0000.0000.0000.0060.0110.0310.1460.0970.0300.0100.1730.0630.130
DURATION OF STAY0.0120.0300.1310.1580.0170.1570.0490.1220.0260.0650.0950.0620.1260.0000.1830.0410.0560.0601.0000.032-0.1520.0400.128-0.1670.1350.0870.0900.0110.0470.020-0.0290.0000.0380.0100.0420.0590.0150.0570.0000.0430.0470.0200.0740.0260.0000.1050.0000.1930.1290.2270.0880.0000.0800.0000.714-0.009
DVT0.0690.0100.0900.0300.0230.0140.0150.0130.0950.0140.0000.0000.0270.0000.0230.0000.0090.0320.0321.0000.1170.0000.0280.0330.0400.0170.0320.0340.0000.0060.0000.0000.0090.0000.0120.0140.0470.0370.0000.3260.0260.0000.0000.0000.0000.0300.0370.0000.0110.0270.0050.0130.0000.0050.0230.000
EF0.2710.048-0.0800.2650.0350.0540.128-0.2830.2250.1410.0390.0230.1720.021-0.2640.0000.0180.114-0.1520.1171.0000.100-0.1350.0740.4160.2420.2990.0730.0210.0000.0850.0470.0000.0550.1900.1970.0390.7570.0560.1080.1950.0110.1310.0350.0330.1650.273-0.1420.181-0.2880.0160.0400.1250.033-0.2160.005
GENDER0.0800.0160.0270.0460.1930.1300.0320.0360.1090.0060.0110.0000.0300.0150.0400.0000.0000.0090.0400.0000.1001.0000.0420.2950.0370.0190.0250.0670.0160.0000.0040.0120.0040.0000.0000.0000.1320.0330.0250.0070.0230.0240.0000.1670.0000.0270.0640.0000.0150.0160.0980.0310.0000.0110.0270.000
GLUCOSE0.0930.0190.1070.1010.0150.0550.0480.0630.1030.0270.0400.0190.0580.0430.1550.0540.0300.3930.1280.028-0.1350.0421.000-0.1190.1100.0630.0760.0900.0000.053-0.0360.0000.0110.0360.0470.0360.0320.0420.0270.0000.0990.0000.0210.0000.0000.0430.0640.1900.0970.2000.0370.0250.0160.0120.1560.016
HB0.0590.023-0.2200.3070.1270.9980.046-0.1420.0450.0590.0260.0300.3030.059-0.2480.0000.0000.139-0.1670.0330.0740.295-0.1191.0000.1650.0850.1290.0840.0300.0460.0420.0000.0000.0520.1340.125-0.0720.0560.0100.0150.0410.7760.0680.1230.0000.1080.096-0.0110.107-0.3370.0880.0000.0180.041-0.161-0.003
HEART FAILURE0.0370.0750.1460.2050.0450.1200.0880.5340.0150.0910.0000.0330.1210.0360.1860.0000.0000.0390.1350.0400.4160.0370.1100.1651.0000.6180.6690.0000.0000.0000.0190.0370.0150.0500.1770.1830.0300.3080.0210.0460.1300.0170.0940.0360.0350.1550.0230.0320.1590.2540.0020.0050.0590.0510.1930.053
HFNEF0.0210.0380.0810.0700.0280.0460.0510.3840.0130.0000.0000.0280.0510.0220.0690.0000.0000.0070.0870.0170.2420.0190.0630.0850.6181.0000.1650.0000.0000.0000.0700.0060.0200.0470.0270.0000.0270.1620.0050.0210.0820.0090.0260.0300.0150.0960.0380.0000.0980.1140.0160.0220.0230.1260.1120.068
HFREF0.0260.0560.1110.1910.0290.1070.0610.3100.0000.1210.0000.0110.1020.0190.1690.0000.0120.0620.0900.0320.2990.0250.0760.1290.6690.1651.0000.0000.0000.0000.0430.0360.0000.0170.1960.2350.0450.2350.0170.0350.0850.0070.1460.0130.0270.1040.0000.0280.1060.2130.0000.0080.0510.0560.1400.082
HTN0.0070.0000.2170.0730.0260.0520.0000.0200.3230.0240.0100.0070.0810.0380.0760.0000.0300.1540.0110.0340.0730.0670.0900.0840.0000.0000.0001.0000.0290.0340.0000.0030.0000.0220.0620.0590.0660.0580.0180.0150.0270.0000.0390.0580.0040.0280.0700.0000.0140.0670.0300.0260.0180.0090.0000.041
INFECTIVE ENDOCARDITIS0.0300.0000.1150.0040.0000.0270.0000.0100.0510.0000.0000.0000.0000.0000.0000.0000.0130.0200.0470.0000.0210.0160.0000.0300.0000.0000.0000.0291.0000.0130.0000.0000.0000.0180.0310.0190.0330.0000.0000.0000.0080.0000.0000.0000.0000.0050.0130.0000.0100.0000.0000.0100.0000.0000.0240.000
Location0.0180.0300.0490.0000.0400.0270.0000.0150.0470.0000.0090.0000.0000.0220.0120.0000.0050.0290.0200.0060.0000.0000.0530.0460.0000.0000.0000.0340.0131.0000.0000.0000.0020.0000.0050.0040.0130.0220.0020.0330.0320.0000.0000.0230.0000.0000.0100.0000.0200.0240.0180.0130.0000.0570.0000.040
MRD No.0.1100.011-0.0680.0380.0330.0110.000-0.0920.0270.0090.0480.0120.0150.021-0.0940.0000.0350.106-0.0290.0000.0850.004-0.0360.0420.0190.0700.0430.0000.0000.0001.0000.0000.0250.0460.0370.0240.0520.0410.0000.0000.0720.0000.0360.0300.0000.0190.0350.0120.031-0.0830.0420.0000.0000.4690.059-0.035
NEURO CARDIOGENIC SYNCOPE0.0470.0000.0000.0100.0000.0110.0010.0280.0170.0070.0000.0080.0130.0200.0160.0200.0070.0000.0000.0000.0470.0120.0000.0000.0370.0060.0360.0030.0000.0000.0001.0000.0340.0090.0280.0240.0000.0000.0290.0120.0310.0000.0100.0000.0000.0170.0180.0000.0070.0000.0000.0200.0000.0210.0080.008
ORTHOSTATIC0.0310.0000.0000.0000.0000.0130.0090.0280.0000.0000.0000.0080.0000.0130.0000.0070.0000.0100.0380.0090.0000.0040.0110.0000.0150.0200.0000.0000.0000.0020.0250.0341.0000.0000.0120.0100.0000.0230.0000.0000.0000.0000.0000.0100.0000.0250.0220.0000.0380.0000.0120.0000.0000.0390.0230.104
OUTCOME_DAMA0.0410.0000.0220.0700.0170.0330.0340.0690.0580.0000.0260.0000.0650.0000.0840.0000.0200.0130.0100.0000.0550.0000.0360.0520.0500.0470.0170.0220.0180.0000.0460.0090.0001.0000.6330.0690.0550.0130.0100.0000.0460.0160.0160.0150.0040.0520.0050.0380.0680.0940.0130.0130.0300.0410.0350.064
OUTCOME_DISCHARGE0.0790.0430.0800.2000.0340.0850.0590.1670.1470.3140.0470.0050.1200.0230.1970.0260.0350.0180.0420.0120.1900.0000.0470.1340.1770.0270.1960.0620.0310.0050.0370.0280.0120.6331.0000.7280.1400.1390.0240.0000.0900.0230.4220.0170.0000.0990.0360.0910.1810.2230.0470.0000.1230.0310.0690.130
OUTCOME_EXPIRY0.0640.0630.0800.1950.0620.0790.0440.1610.1380.4030.0370.0140.0960.0220.1890.0250.0250.0390.0590.0140.1970.0000.0360.1250.1830.0000.2350.0590.0190.0040.0240.0240.0100.0690.7281.0000.1400.1660.0180.0000.0750.0120.5600.0400.0140.0800.0370.0920.1720.2100.0460.0210.1310.0000.0660.211
PLATELETS0.0490.000-0.0410.1160.0340.1570.028-0.0210.0850.0790.0270.0240.0710.020-0.1210.0000.0300.0180.0150.0470.0390.1320.032-0.0720.0300.0270.0450.0660.0330.0130.0520.0000.0000.0550.1400.1401.0000.0580.0180.0000.0140.0710.0880.0000.0000.0500.0360.2070.035-0.1080.0400.0090.0350.041-0.007-0.031
PRIOR CMP0.0360.0370.0420.2050.0000.0350.0600.2920.0810.0990.0190.0030.1120.0130.1990.0000.0070.0540.0570.0370.7570.0330.0420.0560.3080.1620.2350.0580.0000.0220.0410.0000.0230.0130.1390.1660.0581.0000.0190.0340.0040.0000.0970.0000.0270.1030.0060.0260.0900.2220.0060.0000.0960.0360.0930.066
PSVT0.0460.0120.0250.0080.0090.0000.0080.0130.0510.0120.0080.0000.0080.0000.0000.0000.0000.0080.0000.0000.0560.0250.0270.0100.0210.0050.0170.0180.0000.0020.0000.0290.0000.0100.0240.0180.0180.0191.0000.0000.0170.0040.0070.0130.0250.0200.0230.0000.0240.0150.0080.0000.0000.0120.0000.000
PULMONARY EMBOLISM0.0830.0190.0370.0000.0000.0000.0170.0150.1010.0000.0130.0000.0130.0000.0000.0000.0080.0100.0430.3260.1080.0070.0000.0150.0460.0210.0350.0150.0000.0330.0000.0120.0000.0000.0000.0000.0000.0340.0001.0000.0180.0080.0000.0000.0000.0290.0430.0000.0250.0000.0000.0000.0000.0310.0400.029
RAISED CARDIAC ENZYMES0.4050.0000.0740.0800.0000.0440.0650.1370.0690.0420.0000.0150.0350.0280.0710.0000.0000.0280.0470.0260.1950.0230.0990.0410.1300.0820.0850.0270.0080.0320.0720.0310.0000.0460.0900.0750.0140.0040.0170.0181.0000.0130.0560.0090.0100.1230.0780.0270.1730.0800.0250.0170.0260.0310.1070.054
SEVERE ANAEMIA0.0150.0000.0440.0820.0070.3000.0160.0290.0130.0120.0120.0000.0890.0000.1000.0000.0080.0000.0200.0000.0110.0240.0000.7760.0170.0090.0070.0000.0000.0000.0000.0000.0000.0160.0230.0120.0710.0000.0040.0080.0131.0000.0100.0000.0000.0290.0250.0610.0100.1170.0000.0000.0020.0200.0310.019
SHOCK0.0270.0260.0460.1490.0170.0550.0250.1070.0930.6080.0150.0000.0890.0070.1480.0000.0000.0060.0740.0000.1310.0000.0210.0680.0940.0260.1460.0390.0000.0000.0360.0100.0000.0160.4220.5600.0880.0970.0070.0000.0560.0101.0000.0170.0000.0550.0380.0880.1030.1600.0000.0210.0900.0240.0860.141
SMOKING0.0280.0190.1010.0410.3250.0410.0000.0320.0220.0190.0200.0000.0310.0020.0330.0100.0000.0000.0260.0000.0350.1670.0000.1230.0360.0300.0130.0580.0000.0230.0300.0000.0100.0150.0170.0400.0000.0000.0130.0000.0090.0000.0171.0000.0000.0000.0510.0500.0000.0510.0000.0120.0250.0660.0080.039
SSS0.0290.0080.0190.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0350.0150.0270.0040.0000.0000.0000.0000.0000.0040.0000.0140.0000.0270.0250.0000.0100.0000.0000.0001.0000.0190.0000.0000.0000.0000.0000.0000.0000.0090.0000.020
STABLE ANGINA0.2010.0590.0930.1120.0100.0880.0470.1340.1360.0530.0450.0230.0720.0210.1070.0160.0400.0000.1050.0300.1650.0270.0430.1080.1550.0960.1040.0280.0050.0000.0190.0170.0250.0520.0990.0800.0500.1030.0200.0290.1230.0290.0550.0000.0191.0000.0970.0140.3010.1370.0340.0250.0480.0000.1350.048
STEMI0.5340.0240.0860.0410.0390.0600.0640.0000.1670.0780.0590.0050.0590.0080.0480.0110.0290.0060.0000.0370.2730.0640.0640.0960.0230.0380.0000.0700.0130.0100.0350.0180.0220.0050.0360.0370.0360.0060.0230.0430.0780.0250.0380.0510.0000.0971.0000.0210.1770.0580.0160.0280.0470.0410.0820.049
TLC0.0200.0000.0320.0680.0310.0400.0040.0620.0290.0450.0000.0510.0500.0000.1800.0130.0000.0110.1930.000-0.1420.0000.190-0.0110.0320.0000.0280.0000.0000.0000.0120.0000.0000.0380.0910.0920.2070.0260.0000.0000.0270.0610.0880.0500.0000.0140.0211.0000.0370.2080.0000.0000.0320.0110.2400.003
TYPE OF ADMISSION-EMERGENCY/OPD0.1850.0800.1070.1410.0210.0810.0810.1520.0100.0770.0630.0300.0840.0170.1390.0000.0180.0310.1290.0110.1810.0150.0970.1070.1590.0980.1060.0140.0100.0200.0310.0070.0380.0680.1810.1720.0350.0900.0240.0250.1730.0100.1030.0000.0000.3010.1770.0371.0000.1590.0260.0210.0690.0680.2260.034
UREA0.0550.0570.3120.7000.0430.3230.0820.2460.0280.1420.0530.0330.6260.0220.7410.0120.0280.1460.2270.027-0.2880.0160.200-0.3370.2540.1140.2130.0670.0000.024-0.0830.0000.0000.0940.2230.210-0.1080.2220.0150.0000.0800.1170.1600.0510.0000.1370.0580.2080.1591.0000.0920.0000.0690.0140.254-0.002
UTI0.0470.0240.0730.0920.0170.0700.0080.0260.0000.0110.0000.0000.0760.0130.0910.0000.0260.0970.0880.0050.0160.0980.0370.0880.0020.0160.0000.0300.0000.0180.0420.0000.0120.0130.0470.0460.0400.0060.0080.0000.0250.0000.0000.0000.0000.0340.0160.0000.0260.0921.0000.0000.0000.0710.0660.067
VALVULAR0.0420.0890.0260.0130.0000.0020.0150.0110.0790.0130.0060.0000.0000.0250.0180.0000.0160.0300.0000.0130.0400.0310.0250.0000.0050.0220.0080.0260.0100.0130.0000.0200.0000.0130.0000.0210.0090.0000.0000.0000.0170.0000.0210.0120.0000.0250.0280.0000.0210.0000.0001.0000.0000.0360.0180.037
VT0.0520.0000.0260.0610.0080.0120.0180.0560.0050.0970.0220.0000.0240.0000.0610.0000.0060.0100.0800.0000.1250.0000.0160.0180.0590.0230.0510.0180.0000.0000.0000.0000.0000.0300.1230.1310.0350.0960.0000.0000.0260.0020.0900.0250.0000.0480.0470.0320.0690.0690.0000.0001.0000.0070.1090.015
Year0.0370.0920.0230.0150.0820.0430.0000.1150.1200.0810.0190.0300.0220.0190.0150.0290.0720.1730.0000.0050.0330.0110.0120.0410.0510.1260.0560.0090.0000.0570.4690.0210.0390.0410.0310.0000.0410.0360.0120.0310.0310.0200.0240.0660.0090.0000.0410.0110.0680.0140.0710.0360.0071.0000.0000.526
duration of intensive unit stay0.0680.0690.1420.1800.0120.1570.0660.1400.0000.0630.0980.0330.1310.0000.2130.0120.0610.0630.7140.023-0.2160.0270.156-0.1610.1930.1120.1400.0000.0240.0000.0590.0080.0230.0350.0690.066-0.0070.0930.0000.0400.1070.0310.0860.0080.0000.1350.0820.2400.2260.2540.0660.0180.1090.0001.0000.057
month0.0510.071-0.0190.0550.0420.0230.0180.0150.1390.1490.0210.0320.0290.0310.0140.0000.0670.130-0.0090.0000.0050.0000.016-0.0030.0530.0680.0820.0410.0000.040-0.0350.0080.1040.0640.1300.211-0.0310.0660.0000.0290.0540.0190.1410.0390.0200.0480.0490.0030.034-0.0020.0670.0370.0150.5260.0571.000

Missing values

2025-11-25T22:40:56.535519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T22:40:56.980668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MRD No.D.O.AD.O.DAGEGENDERLocationTYPE OF ADMISSION-EMERGENCY/OPDmonth yearDURATION OF STAYduration of intensive unit staySMOKINGALCOHOLDMHTNCADPRIOR CMPCKDHBTLCPLATELETSGLUCOSEUREACREATININEBNPRAISED CARDIAC ENZYMESEFSEVERE ANAEMIAANAEMIASTABLE ANGINAACSSTEMIATYPICAL CHEST PAINHEART FAILUREHFREFHFNEFVALVULARCHBSSSAKICVA INFRACTCVA BLEEDAFVTPSVTCONGENITALUTINEURO CARDIOGENIC SYNCOPEORTHOSTATICINFECTIVE ENDOCARDITISDVTCARDIOGENIC SHOCKSHOCKPULMONARY EMBOLISMCHEST INFECTIONOUTCOME_DAMAOUTCOME_DISCHARGEOUTCOME_EXPIRYmonthYear
SNO
1234735.02017-01-042017-03-04810002017-04-013200100009.516.1337.080.034.00.901880.0135.00101001100000000000000000000.0FalseTrueFalse12017
2234696.02017-01-042017-05-04650002017-04-0152010110013.79.0149.0112.018.00.90475.0042.00000000000000000100000000000.0FalseTrueFalse12017
3234882.02017-01-042017-03-04530102017-04-0133001010010.614.7329.0187.093.02.30210.0042.00000001100001000000000000000.0FalseTrueFalse12017
4234635.02017-01-042017-08-04671102017-04-0186000110012.89.9286.0130.027.00.60475.0042.00000000000000000000000000000.0FalseTrueFalse12017
5234486.02017-01-042018-07-05601102017-04-01239000101013.69.126.0144.055.01.251840.0016.00000000000000000000000000000.0FalseTrueFalse12017
6234675.02017-01-042017-10-04440102017-04-01108001111013.522.3322.0217.051.00.901720.0025.00001001100000000100000000000.0FalseTrueFalse12017
7234563.02017-01-042017-06-04561102017-04-0162001111013.312.6166.0277.028.00.60518.0030.00001101100000000000000000000.0FalseTrueFalse12017
8208455.02017-01-042018-07-05470102017-04-01139011100012.69.5328.0159.030.01.00475.0045.00000000000000000000000000000.0FalseTrueFalse12017
967070.02017-01-042017-03-04651102017-04-0133000100012.410.1225.0156.035.01.00475.0060.00001001010000000100000000100.0FalseFalseTrue12017
10153218.02017-01-042017-03-04590102017-04-0131001110011.44.8173.0200.029.00.80475.0042.00000000000000000000000000000.0FalseTrueFalse12017
MRD No.D.O.AD.O.DAGEGENDERLocationTYPE OF ADMISSION-EMERGENCY/OPDmonth yearDURATION OF STAYduration of intensive unit staySMOKINGALCOHOLDMHTNCADPRIOR CMPCKDHBTLCPLATELETSGLUCOSEUREACREATININEBNPRAISED CARDIAC ENZYMESEFSEVERE ANAEMIAANAEMIASTABLE ANGINAACSSTEMIATYPICAL CHEST PAINHEART FAILUREHFREFHFNEFVALVULARCHBSSSAKICVA INFRACTCVA BLEEDAFVTPSVTCONGENITALUTINEURO CARDIOGENIC SYNCOPEORTHOSTATICINFECTIVE ENDOCARDITISDVTCARDIOGENIC SHOCKSHOCKPULMONARY EMBOLISMCHEST INFECTIONOUTCOME_DAMAOUTCOME_DISCHARGEOUTCOME_EXPIRYmonthYear
SNO
15747699270.02018-07-012019-01-04721102019-03-0133000100110.920.5246.0138.05.81.12500.0135.00001001100000000000000001100.0FalseFalseTrue72019
15748303051.02019-03-302019-04-01611102019-03-0131000110012.48.4243.0110.017.00.818.0060.00000010000000000000000000000.0FalseTrueFalse32019
15749273016.02019-03-302019-03-31741102019-03-0122000110012.410.1225.0136.035.01.0475.0060.00000000000000000000000000000.0FalseTrueFalse32019
15750698545.02019-03-302019-04-03521102019-03-0153001010012.37.4190.095.027.00.9475.0060.00010000000000000000000000000.0FalseTrueFalse32019
15751502577.02019-03-302019-03-31371012019-03-0120000000014.38.4475.092.015.00.5475.0060.00000010000000000000000000000.0FalseTrueFalse32019
15752469963.02019-03-312019-04-08601102019-03-019900001008.125.063.0222.018.00.51170.0138.00100001010000000000000000000.0FalseTrueFalse32019
15753699585.02019-03-312019-04-04861112019-03-015100111008.813.7361.0131.057.01.4292.0138.00101000000000000000000000000.0FalseTrueFalse32019
15754699500.02018-07-012019-01-04500002019-03-0122001101013.215.6142.0248.094.01.8206.0028.00000000000001000000000000000.0FalseFalseTrue72019
15755700415.02019-03-312019-04-09820102019-03-0110500011009.311.7372.0210.067.01.91120.0032.00100001010001000000000000000.0FalseTrueFalse32019
15756699524.02019-03-312019-04-03591112019-03-0142000110013.112.5431.0153.029.00.878.0060.00000000000000000000000000000.0FalseTrueFalse32019